SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 476–500 of 800 papers

TitleStatusHype
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System—0
Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration—0
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks—0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic ApproachCode0
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System—0
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems—0
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection—0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification—0
A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks—0
Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold—0
Orthogonal variance-based feature selection for intrusion detection systems—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
From Zero-Shot Machine Learning to Zero-Day Attack Detection—0
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks—0
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach—0
A Novel Online Incremental Learning Intrusion Prevention System—0
Modern Cybersecurity Solution using Supervised Machine Learning—0
Integrating Sensing and Communication in Cellular Networks via NR Sidelink—0
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT—0
Feature Analysis for Machine Learning-based IoT Intrusion Detection—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified